ArticleNature communications2023
A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed, 89 citations in OpenAlex.
- Article
- Hematopoietic stem cell aging: a review of transcriptional and multi-omics insights and potential paths for AI integration.Experimental & molecular medicine · 2026Review
- SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling.bioRxiv : the preprint server for biology · 2026Article
- The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime.Nucleic acids research · 2026Article
- Trajectory-guided dimensionality reduction for multi-sample single-cell RNA-seq data reveals biologically relevant sample-level heterogeneity.Bioinformatics (Oxford, England) · 2026Article
- Laminar patterning transcription factors orchestrate spatial metabolite partitioning in Capsicum fruit.Nature plants · 2026Article
- Transcriptional regulators predicted to drive macrophage dysregulation during impaired wound healing in diabetic mice.bioRxiv : the preprint server for biology · 2026Article
- Shape-constrained, changepoint additive models for time series omics data with cpam.Nucleic acids research · 2026Article
- Systematic background selection with BasCoD enhances contrastive dimension reduction in single cell genomics.Nature communications · 2026Article
- Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer.International journal of molecular medicine · 2026Article
- Single-cell transcriptome sequencing revealing the microenvironment of breast fibroepithelial tumor.Biochemistry and biophysics reports · 2026Article
- PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns.Genome biology · 2026Article
- Pseudotime graph diffusion for post hoc visualization of inferred single-cell trajectories.bioRxiv : the preprint server for biology · 2026Article
- Interpretable trajectory inference with single-cell linear adaptive negative-binomial expression (scLANE) testing.Nucleic acids research · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- A self-supervised machine learning pipeline for extracting information from live cell images at multiple doses and timepoints.Scientific reports · 2026Article
- Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.Briefings in bioinformatics · 2026Review
- Pseudotimecascade Visualizes Gene Expression Cascade in Pseudotime Analysis.Computational and structural biotechnology journal · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- A novel paradigm for single-cell annotation in stem cell research.Stem cell reports · 2025Review
2 more citing papers are in PubMed but not listed here.
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6 authors at 2 institutions in 1 country.
Funding
Abstract
Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.